Hierarchical partition models (see Malec and Sedransk, 1992, Consonni and Veronese, 1995) aim at finding an optimal grouping (partition) of a set of experiments regarding a target variable. In this class of models the partition is regarded as an unknown parameter, and one of the main goals is computing the posterior distribution over the class of the possible partitions. This problem has been addressed in Sampietro and Veronese (1998), where a Metropolis-Hastings algorithm is applied. In this paper the performance of an alternative procedure, based on the logic of genetic algorithms, is evaluated. The results of the two approaches are compared, even if a conjoint use of them is to be advised.

Borroni, C., Piccarreta, R. (2001). Genetic algorithms for the analysis of Bayesian hierarchical partition models. STATISTICAL METHODS & APPLICATIONS, 10, 113-121 [10.1007/BF02511643].

Genetic algorithms for the analysis of Bayesian hierarchical partition models

BORRONI, CLAUDIO GIOVANNI;
2001

Abstract

Hierarchical partition models (see Malec and Sedransk, 1992, Consonni and Veronese, 1995) aim at finding an optimal grouping (partition) of a set of experiments regarding a target variable. In this class of models the partition is regarded as an unknown parameter, and one of the main goals is computing the posterior distribution over the class of the possible partitions. This problem has been addressed in Sampietro and Veronese (1998), where a Metropolis-Hastings algorithm is applied. In this paper the performance of an alternative procedure, based on the logic of genetic algorithms, is evaluated. The results of the two approaches are compared, even if a conjoint use of them is to be advised.
Articolo in rivista - Articolo scientifico
Bayesian inference - hierarchical partition models - genetic algorithms - Metropolis Hastings algorithms - MCMC methods
English
2001
10
113
121
none
Borroni, C., Piccarreta, R. (2001). Genetic algorithms for the analysis of Bayesian hierarchical partition models. STATISTICAL METHODS & APPLICATIONS, 10, 113-121 [10.1007/BF02511643].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/681
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